\chapter{Model-based Reinforcement Learning}

\section{Adaptive and Learning MPC}

% cover classical work on adaptive LQR; exploration/persistent excitation (becker/kumar 85, possibly results from adaptive control books?)

% survey, briefly, modern work on adaptive LQR. Model-based and model-free? Lots of refs from szepesvari and recht papers. 

% recht papers have SLS as a prereq

% include output feedback? Pixels to torques, e.g.

% Bayesian methods. PETS? Dreamer and related models


% Tomlin work + others; linear learning-based MPC only. Survey or dig into details?
% Rosolia work on learning MPC

% Iterative Learning Control?

% Episodic fitting of linear models


\section{Combining Model and Policy Learning}

% Discuss PILCO, other probabilistic methods like PETS?

% Discuss predictive models for observations, e.g. visuomotor control
% Should avoid introduction of latent variable models in the class, but can include for the future. Could survey representation learning in MB RL?

% optimizing model via backprop through value; how value learning and model learning mesh

\section{Bibliographic Notes}

% For learning MPC, Hewing review